Intuitive Cognition and Models of Human-Automation Interaction

Area of Science:

• Cognitive Psychology
• Human-Computer Interaction
• Systems Engineering

Background:

• Human reasoning and decision-making are largely intuitive.
• Understanding intuitive cognition is crucial for designing effective human-machine systems.

Purpose of the Study:

• Analyze the implications of intuitive cognition dominance on human-automation interaction models.
• Focus on the Parasuraman et al. model and taxonomy.
• Propose a new taxonomy incorporating intuitive cognition.

Main Methods:

• Conducted a literature review of 120 articles and books.
• Included works published within the last 50 years.
• Selected relevant and commonly cited literature.

Main Results:

• Intuitive cognition consistently dominates human reasoning and decision-making across examined situations.
• The implications for the Parasuraman et al. model and taxonomy were analyzed.
• A novel taxonomy for human-automation interaction was proposed.

Conclusions:

• Understanding the intuitive nature of human cognition is key for future human-automation interaction models.
• The proposed taxonomy integrates intuitive cognition for improved system design.
• This research offers insights for developing more effective human-machine systems.